Neuroevolution is a technique that combines evolutionary algorithms with neural networks to improve the training of reinforcement learning (RL) models. It helps RL by automatically discovering optimal or near-optimal neural network architectures and parameters for specific tasks without requiring extensive manual tuning. This is especially beneficial in environments where the optimal solutions are not apparent or are difficult to derive through traditional methods. Neuroevolution uses a population of neural networks, iteratively evaluating their performance on the RL task and selecting the best-performing individuals for mutation and crossover, which can lead to better-performing networks over generations.
One of the main advantages of neuroevolution in reinforcement learning is its ability to work well in high-dimensional or complex action spaces. For instance, tasks in video game environments or robotic control problems often involve a vast number of potential network configurations. Traditional RL methods, like Q-learning or policy gradients, may struggle to optimize these networks efficiently, often requiring a lot of hyperparameter adjustments or getting stuck in local optima. Neuroevolution can explore the solution space more broadly, leveraging genetic algorithms to guide the search and find effective network architectures, even without a predefined exploration strategy.
Moreover, neuroevolution facilitates the development of adaptive agents that can perform well in dynamic or uncertain environments. Since the neural networks can evolve over time, they can adapt to changes in the environment, such as new obstacles in a robot navigation task or changing levels in a game. A concrete example is when researchers have used neuroevolution to train AI agents that play complex games like "Mario Bros." or "Flappy Bird." By varying the network architectures and optimizing through evolutionary strategies, the agents learned not only how to play but also how to adapt to various game scenarios effectively, outperforming hand-tuned agents. Thus, neuroevolution provides a powerful and flexible approach to enhancing reinforcement learning applications.